class_docstr: Concept Activation Vector (CAV) is a vector orthogonal to the decision
  boundary of a classifier which distinguishes between activation
  vectors produced by different concepts.
  More details can be found in the paper:
      https://arxiv.org/abs/1711.11279
docstr: A utility method for assembling filename and its path, from
  a concept list and a layer name.
  
  Args:
      path (str): A path to be concatenated with the concepts key and
              layer name.
      model_id (str): A unique model identifier associated with input
              `layer` and `concepts`
      concepts (list[Concept]): A list of concepts that are concatenated
              together and used as a concept key using their ids. These
              concept ids are retrieved from TCAV s`Concept` objects.
      layer (str): The name of the layer for which the activations are
              computed.
  
  Returns:
      cav_path(str): A string containing the path where the computed CAVs
              will be stored.
              For example, given:
                  concept_ids = [0, 1, 2]
                  concept_names = ["striped", "random_0", "random_1"]
                  layer = "inception4c"
                  path = "/cavs",
              the resulting save path will be:
                  "/cavs/default_model_id/0-1-2-inception4c.pkl"
docstr_tok: ["A","utility","method","for","assembling","filename","and","its","path",",","from","a","concept","list","and","a","layer","name",".","Args",":","path","(","str",")",":","A","path","to","be","concatenated","with","the","concepts","key","and","layer","name",".","model_id","(","str",")",":","A","unique","model","identifier","associated","with","input","`","layer","`","and","`","concepts","`","concepts","(","list","[","Concept","]",")",":","A","list","of","concepts","that","are","concatenated","together","and","used","as","a","concept","key","using","their","ids",".","These","concept","ids","are","retrieved","from","TCAV","s","`","Concept","`","objects",".","layer","(","str",")",":","The","name","of","the","layer","for","which","the","activations","are","computed",".","Returns",":","cav_path","(","str",")",":","A","string","containing","the","path","where","the","computed","CAVs","will","be","stored",".","For","example",",","given",":","concept_ids","=","[","0",",","1",",","2","]","concept_names","=","[","``","striped","''",",","``","random_0","''",",","``","random_1","''","]","layer","=","``","inception4c","''","path","=","``","\/cavs","''",",","the","resulting","save","path","will","be",":","``","\/cavs\/default_model_id\/0-1-2-inception4c.pkl","''"]
code: def assemble_save_path(
      path: str, model_id: str, concepts: List[Concept], layer: str
  ) -> str:
      r"""
      A utility method for assembling filename and its path, from
      a concept list and a layer name.
  
      Args:
          path (str): A path to be concatenated with the concepts key and
                  layer name.
          model_id (str): A unique model identifier associated with input
                  `layer` and `concepts`
          concepts (list[Concept]): A list of concepts that are concatenated
                  together and used as a concept key using their ids. These
                  concept ids are retrieved from TCAV s`Concept` objects.
          layer (str): The name of the layer for which the activations are
                  computed.
  
      Returns:
          cav_path(str): A string containing the path where the computed CAVs
                  will be stored.
                  For example, given:
                      concept_ids = [0, 1, 2]
                      concept_names = ["striped", "random_0", "random_1"]
                      layer = "inception4c"
                      path = "/cavs",
                  the resulting save path will be:
                      "/cavs/default_model_id/0-1-2-inception4c.pkl"
  
      """
  
      file_name = concepts_to_str(concepts) + "-" + layer + ".pkl"
      return os.path.join(path, model_id, file_name)
  
file_path: captum/captum/concept/_core/cav.py
imports: import os
  from typing import Any, Dict, List
  import torch
  from captum.concept._core.concept import Concept
  from captum.concept._utils.common import concepts_to_str
label_desc: Use image node_id 2 for calling the CAV obj's underlying member method code with example usage: obj.assemble_save_path(path, model_id, concepts, layer) and returns: os